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Joint Feature Generation and Open-set Prototype Learning for generalized zero-shot open-set classification
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DOI：10.1016/j.patcog.2023.110133.png)
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In generalized zero-shot classification, test samples can belong to either seen or unseen classes. However, in real-world situations, there may be many open-set samples in the test set where neither visual nor semantic representations of the classes are provided. The new problem is defined as generalized zero-shot open-set classification (GZSOSC). The purpose is to tell whether an instance belongs to which seen or unseen classes, or to reject an instance if it belongs to the open-set classes. To address this problem, we propose a novel method called Joint Feature Generation and Open-Set Prototype Learning (JFGOPL) for GZSOSC tasks. JFGOPL is presented to combine GAN training with open-set prototype learning, where the former generates high-quality unseen and open-set samples and the latter learns some open-set prototypes. Specifically, a novel GAN training strategy is proposed, where an intra-class compactness loss and an inter-class dispersion loss are proposed to ensure the discrimination of the generated samples and to make the learned embedding network less susceptible to the domain shift problem. Furthermore, open-set prototypes are derived by projecting confident open-set samples into the semantic space using the updated embedding network. Experiments on widely used benchmarks demonstrate the superiority of JFGOPL over existing methods for tackling the challenging GZSOSC problem.
Keyword:
Generalized zero-shot open-set classification
Feature generation
Open-set prototype learning
Intra-class compactness loss
Inter-class dispersion loss
期刊
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7.6
论文数:
1.3W
被引数:
4.5W
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